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P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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P300-based brain-computer interface for communication in assistive technology centres: influence of users' profile on
Galiotta Valentina1,2, Caracci Valentina1,3, Toppi Jlenia1,3
1Neuroelectric Imaging and BCI Laboratory, Santa Lucia Foundation, Rome, Italy.
Journal of Neural Engineering
|June 2, 2025
Summary
Brain-computer interfaces (BCI) show promise as assistive technology (AT). A study found that a user's neuropsychological profile significantly impacts BCI control accuracy for communication.
Area of Science:
- Neuroscience
- Rehabilitation Engineering
- Assistive Technology
Background:
- Assistive technology (AT) enhances independence and daily life participation.
- Brain-computer interfaces (BCI) offer an alternative output using neurophysiological signals to control external devices.
Purpose of the Study:
- To screen patients at an AT center for BCI eligibility.
- To identify factors influencing BCI control performance.
Main Methods:
- Thirty-five users and 11 healthy subjects participated.
- Participants operated a P300-speller BCI system.
- Evaluated influence of clinical diagnosis, socio-demographics, dependence, disability, and neuropsychological profile on BCI performance.
Main Results:
- 7.1% of users achieved functional communication accuracy (mean 93.6 ± 8.0%).
- 8 users had online accuracy below 70%.
- Neuropsychological profile significantly affected online accuracy and information transfer rate (ITR).
Conclusions:
- A notable percentage of users achieved functional BCI accuracy, indicating BCI effectiveness.
- Neuropsychological factors are key determinants of BCI performance.
- Results support BCI integration into AT centers for communication and multimodal access.
